Constraint-based methods relate to approaches like [1]} PC and Fast Causal Inference (FCI) that test conditional independence between factors to assess their causal links. The main intuition is that two statistically independent variables are not causally linked. First, pairwise independence is evaluate to determine the undirected structure. Following this conditional independence is tested to orient the links between the nodes. If two nodes fail this test, then they can be added to the separation set of each other used to orient colliders - nodes of the causal graph with two or more incoming links. The main difference between the aforementioned PC and FCI algorithms is FCI's ability to be asymptotically correct in the presence of confounders. Both of them are however limited to the causal equivalence classes, i.e., causal structures that satisfy the same conditional Independence. A method that searches over the space of possible equivalence classes is the Greedy Equivalence Search (GES) [2]} officially considered a score-based method which uses the Bayesian Information Criterion (BIC).
